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Econometrics and data science = appl...

Nokeri, Tshepo Chris.

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Econometrics and data science = apply data science techniques to model complex problems and implement solutions for economic problems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Econometrics and data science/ by Tshepo Chris Nokeri.
Reminder of title:	apply data science techniques to model complex problems and implement solutions for economic problems /
Author:	Nokeri, Tshepo Chris.
Published:	Berkeley, CA :Apress : : 2022.,
Description:	xviii, 228 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1 Introduction to Econometrics -- Chapter 2 Univariate Consumption Study Applying Regression -- Chapter 3 Multivariate Consumption Study Applying Regression -- Chapter 4 Forecasting Growth -- Chapter 5 Classifying Economic Data Applying Logistic Regression -- Chapter 6 Finding Hidden Patterns in World Economy and Growth -- Chapter 7 Clustering GNI Per Capita on a Continental Level -- Chapter 8 Solving Economic Problems Applying Artificial Neural Networks -- Chapter 9 Inflation Simulation -- Chapter 10 Economic Causal Analysis Applying Structural Equation Modelling.
Contained By:	Springer Nature eBook
Subject:	Econometrics. -
Online resource:	https://doi.org/10.1007/978-1-4842-7434-7
ISBN:	9781484274347

Econometrics and data science = apply data science techniques to model complex problems and implement solutions for economic problems /
Nokeri, Tshepo Chris.

Econometrics and data scienceapply data science techniques to model complex problems and implement solutions for economic problems /[electronic resource] :by Tshepo Chris Nokeri. - Berkeley, CA :Apress :2022. - xviii, 228 p. :ill., digital ;24 cm.

Chapter 1 Introduction to Econometrics -- Chapter 2 Univariate Consumption Study Applying Regression -- Chapter 3 Multivariate Consumption Study Applying Regression -- Chapter 4 Forecasting Growth -- Chapter 5 Classifying Economic Data Applying Logistic Regression -- Chapter 6 Finding Hidden Patterns in World Economy and Growth -- Chapter 7 Clustering GNI Per Capita on a Continental Level -- Chapter 8 Solving Economic Problems Applying Artificial Neural Networks -- Chapter 9 Inflation Simulation -- Chapter 10 Economic Causal Analysis Applying Structural Equation Modelling.

Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models.

ISBN: 9781484274347

Standard No.: 10.1007/978-1-4842-7434-7doiSubjects--Topical Terms:

542934
Econometrics.

LC Class. No.: HB139 / .N65 2022

Dewey Class. No.: 330.015195

Econometrics and data science = apply data science techniques to model complex problems and implement solutions for economic problems /
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505 0 $a Chapter 1 Introduction to Econometrics -- Chapter 2 Univariate Consumption Study Applying Regression -- Chapter 3 Multivariate Consumption Study Applying Regression -- Chapter 4 Forecasting Growth -- Chapter 5 Classifying Economic Data Applying Logistic Regression -- Chapter 6 Finding Hidden Patterns in World Economy and Growth -- Chapter 7 Clustering GNI Per Capita on a Continental Level -- Chapter 8 Solving Economic Problems Applying Artificial Neural Networks -- Chapter 9 Inflation Simulation -- Chapter 10 Economic Causal Analysis Applying Structural Equation Modelling.
520 $a Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models.
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